Q-omics provides the consensus-scored SYCP3 profile across patient tissues and cancer cell-line models. SYCP3 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SYCP3 is differentially expressed in 8, with the highest sampling consensus in LIHC. Additionally, SYCP3 RNA expression shows 19,785 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight ACC, LIHC, and UVM as cancer lineages where SYCP3 shows reproducible signals across survival, tumor–normal expression, and patient cross-omics analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns.
Premium analyses for SYCP3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SYCP3 survival associations across molecular data types. SYCP3 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (3) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SYCP3 RNA expression–survival associations across cancer types. High SYCP3 expression shows unfavorable associations in MESO and UVM, but favorable associations in ACC, BRCA, HNSC and BLCA. The ACC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify ACC as the clearest survival context for SYCP3 RNA expression.
This table summarizes SYCP3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 5. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SYCP3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SYCP3 shows lower tumor expression in LUSC, LUAD and BRCA and higher tumor expression in LIHC, KICH and THCA. The LIHC box plot shows higher SYCP3 RNA expression in tumor versus normal tissue (log2 FC = +0.085, t-test p < 0.001).
This table shows molecular features associated with SYCP3 in patient tissues and cancer cell lines. In patient samples, SYCP3 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, SYCP3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and LARGE_INTESTINE.
